arXiv research

Researchers calculated EVaR for various distributions using Lambert function.

The paper mentioned in the title introduces the entropic value at risk. I give some extra comments and using the general theory make a relation with some commonotone risk measures.

Paper introduces Lambda EVaR, a new risk measure.

Efficiently computes optimal policies for Entropic Risk Measures.

The entropic value-at-risk (EVaR) is a new coherent risk measure, which is an upper bound for both the value-at-risk (VaR) and conditional value-at-risk (CVaR). As important properties, the EVaR is strongly monotone over its domain and strictly monotone over a broad sub-domain including all continuous distributions, wh…

We generalize Quasi-Linear Means by restricting to the tail of the risk distribution and show that this can be a useful quantity in risk management since it comprises in its general form the Value at Risk, the Tail Value at Risk and the Entropic Risk Measure in a unified way. We then investigate the fundamental propert…

This paper is devoted to study the optimal portfolio problem. Harry Markowitz's Ph.D. thesis prepared the ground for the mathematical theory of finance. In modern portfolio theory, we typically find asset returns that are modeled by a random variable with an elliptical distribution and the notion of portfolio risk is d…

Optimizes portfolios with GM returns using convex optimization.

New risk measures control subgroup imbalances, improving PAC-Bayesian bounds.

Sharp bounds found for various risk measures using generalized FGM copulas.

The paper introduces a new risk assessment framework using φ-divergence.

The paper studies risk-sensitive learning schemes and provides learning bounds for empirical OCE minimizers.

In this paper we derive robust super- and subhedging dualities for contingent claims that can depend on several underlying assets. In addition to strict super- and subhedging, we also consider relaxed versions which, instead of eliminating the shortfall risk completely, aim to reduce it to an acceptable level. This yie…

Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.

Introduces entropic tribrackets and their applications in link distinguishing.

We define link and graph invariants from entropic magmas modeling them on the Kauffman bracket and Tutte polynomial. We define the homology of entropic magmas. We also consider groups that can be assigned to the families of compatible entropic magmas.

A method for calculating multi-portfolio time consistent multivariate risk measures in discrete time is presented. Market models for $d$ assets with transaction costs or illiquidity and possible trading constraints are considered on a finite probability space. The set of capital requirements at each time and state is c…

The paper analyzes stability and convergence rates of entropic and Sinkhorn potentials.

Study of Gaussian distributions using entropic Gromov-Wasserstein and inner product Gromov-Wasserstein.

A new RL framework for risk-sensitive decision-making using convex scoring functions.

Improved neural framework for scaling entropic MOT with significant computational gains.

Risk measures for multivariate financial positions are studied in a utility-based framework. Under a certain incomplete preference relation, shortfall and divergence risk measures are defined as the optimal values of specific set minimization problems. The dual relationship between these two classes of multivariate ris…

Equivalent characterizations of multiportfolio time consistency are deduced for closed convex and coherent set-valued risk measures on $L^p(Ω,\mathcal F, P; R^d)$ with image space in the power set of $L^p(Ω,\mathcal F_t,P;R^d)$. In the convex case, multiportfolio time consistency is equivalent to a cocycle condition on…

A new framework tightens risk measure confidence bounds.

Entropic herding generates smooth distributions for probabilistic modeling.

The paper explains emergent phenomena in deep learning using entropic forces.

The paper studies convergence rates of Tsallis entropic regularization in optimal transport.

Paper generalizes Bakry-Émery calculus for curvature and applies to Markov chains.

Paper explores entropic curvature in Markov chains, comparing it to other curvatures.

ERTS uses Thompson sampling for Gaussian entropic risk bandits, achieving regret bounds.

An Entropic Dynamics of exchange rates is laid down to model the dynamics of foreign exchange rates, FX, and European Options on FX. The main objective is to represent an alternative framework to model dynamics. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where…

Study entropic regularization of Gaussian measures and processes on Hilbert space.

Efficiently estimates optimal transport maps with rigorous guarantees.

New risk measures incorporate economic states to assess crude oil derivatives.

This work addresses time inconsistency in risk measures and develops a dynamic programming principle for risk minimization problems.

The paper connects tempering and entropic mirror descent for sampling.

Unified framework for risk-aware policy learning in contextual bandits.

We prove several fundamental statistical bounds for entropic OT with the squared Euclidean cost between subgaussian probability measures in arbitrary dimension. First, through a new sample complexity result we establish the rate of convergence of entropic OT for empirical measures. Our analysis improves exponentially o…

In this paper, we obtain fundamental $\mathcal{L}_{p}$ bounds in sequential prediction and recursive algorithms via an entropic analysis. Both classes of problems are examined by investigating the underlying entropic relationships of the data and/or noises involved, and the derived lower bounds may all be quantified in…

Using elements from the theory of ergodic backward stochastic differential equations (BSDE), we study the behavior of forward entropic risk measures. We provide their general representation results (via both BSDE and convex duality) and examine their behavior for risk positions of long maturities. We show that forward …

The paper improves convergence for linear systems using entropic mirror descent with Polyak stepsizes.

This research improves value-at-risk estimation during financial crises using non-extensive statistical methods.

In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types of entropy, includin…

Researchers develop a method to infer reference measures from observed functionals.

New method corrects bias in estimating entropic risk for better decision-making.

Solving linear programs by using entropic penalization has recently attracted new interest in the optimization community, since this strategy forms the basis for the fastest-known algorithms for the optimal transport problem, with many applications in modern large-scale machine learning. Crucial to these applications h…

Optimizes shortfall risk using gradient-based methods.

Unified framework for optimal transport on curved spaces using neural potentials.